[R] se's and CI's for fitted lines in multivariate regression analysis

[Sigrid]

Okay, I've now tried to the predict function and get the SE, although it seem
to calculate SE for each observation from the line (I assume), while I want
the CI-interval and SE for each line fitted line for the treatment. I do not
really understand what  parameter mean these SEs are calculated from when
there would be several means along the line...?. This is what I get with
predict:

> predict(model, se.fit = TRUE, interval = "confidence")

Another way I can think of to show how well the lines fit the data is to
look at the intercepts and slopes instead. I can specify the line for each
level and would then get the estimate of slope and intercept, although I do
not know how I show the standard errors of the slope and intercept. 
lm(decrease[treatment=="A"]~colpos[treatment=="A"])

Call:
lm(formula = decrease[treatment == "A"] ~ colpos[treatment ==  "A"])

Coefficients:
             (Intercept)  colpos[treatment == "A"]  
                  2.5357                    0.4643  

Please let me know if you know how to find st. errors for (or st. error for
slope and intercept) of lines for each factor of a treatment.

Thank you
~S




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